scTPA

scTPA computes pathway activation signatures from single-cell RNA sequencing (RNA-seq) data to enable functional interpretation and annotation of cell clusters.

Key Features:

• Pathway-Based Analysis: Leverages prior biological pathway knowledge and an extensive collection of biological pathways, categorized by functional and taxonomic classifications, to analyze single-cell RNA-seq data for human and mouse models.
• Gene Set Enrichment Methods: Incorporates four widely-used gene set enrichment methods to estimate pathway activation scores for individual cells.
• Clustering Analysis and Cell-Type-Specific Pathway Identification: Performs clustering analysis to identify cell-type-specific activation pathways for functional interpretation of cellular heterogeneity.

Scientific Applications:

• Developmental biology: Identifies pathway activation patterns across cell clusters to study developmental processes.
• Immunology: Reveals pathway signatures in immune cell subsets to support immunological studies.
• Cancer research: Detects pathway activation heterogeneity within tumor single-cell datasets to aid cancer research.
• Single-cell heterogeneity analysis: Dissects cellular heterogeneity by identifying pathway-based functional states across cells.

Methodology:

Leverages prior pathway knowledge and an extensive, categorized pathway collection, applies four gene set enrichment methods to compute pathway activation scores per cell from single-cell RNA-seq data, and uses clustering analysis to identify cell-type-specific activated pathways.